{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Twist_s128_e2_e20\n",
    "\n",
    "> Twist layer + Mish + MaxBlurPool + restrick"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# setup and imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install git+https://github.com/ayasyrev/model_constructor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install git+https://github.com/kornia/kornia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imagenette_experiments.train_utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from kornia.contrib import MaxBlurPool2d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.basic_train import *\n",
    "from fastai.vision import *\n",
    "# from fastai.script import *\n",
    "from model_constructor.net import *\n",
    "from model_constructor.layers import SimpleSelfAttention, ConvLayer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Twist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def conv(ni, nf, ks=3, stride=1, bias=False):\n",
    "    return nn.Conv2d(ni, nf, kernel_size=ks, stride=stride, padding=ks//2, bias=bias)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class conv_twist(Module):\n",
    "    def __init__(self, ni, nf, stride=1):\n",
    "        # self.radii = nn.Parameter(torch.Tensor(nf), requires_grad=True)\n",
    "        self.center_x = nn.Parameter(torch.Tensor(nf), requires_grad=True)\n",
    "        self.center_y = nn.Parameter(torch.Tensor(nf), requires_grad=True)\n",
    "        # self.radii.data.uniform_(0.3, 0.7)\n",
    "        self.center_x.data.uniform_(-0.7, 0.7)\n",
    "        self.center_y.data.uniform_(-0.7, 0.7)\n",
    "        self.conv = conv(ni, nf, stride=stride)\n",
    "        self.convx = conv(ni, nf, stride=stride)\n",
    "        self.convy = conv(ni, nf, stride=stride)\n",
    "        self.convx.weight.data = (self.convx.weight - self.convx.weight.flip(2).flip(3)) / 2\n",
    "        self.convy.weight.data = self.convx.weight.transpose(2,3).flip(2)\n",
    "\n",
    "    def forward(self, x):\n",
    "        self.convx.weight.data = (self.convx.weight - self.convx.weight.flip(2).flip(3)) / 2  # make convx a first-order operator by symmetrizing it\n",
    "        self.convy.weight.data = (self.convy.weight - self.convy.weight.flip(2).flip(3)) / 2\n",
    "        # self.convy.weight.data = self.convx.weight.transpose(2,3).flip(2))                    # make convy a 90 degree rotation of convx\n",
    "        x1 = self.conv(x)\n",
    "        _, c, h, w = x1.size()\n",
    "        XX = torch.from_numpy(np.indices((1,h,w))[2]*2/w).type(x.dtype).to(x.device) - self.center_x.view(-1,1,1)\n",
    "        YY = torch.from_numpy(np.indices((1,h,w))[1]*2/h).type(x.dtype).to(x.device) - self.center_y.view(-1,1,1)\n",
    "        # mask = ramp_func((XX**2+YY**2)/(self.radii.type(x.dtype).to(x.device).view(-1,1,1)**2))\n",
    "        return x1 + (XX * self.convx(x) + YY * self.convy(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ResBlock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class NewLayer(nn.Sequential):\n",
    "    \"\"\"Basic conv layers block\"\"\"\n",
    "    def __init__(self, ni, nf, ks=3, stride=1,\n",
    "            act=True,  act_fn=nn.ReLU(inplace=True),\n",
    "            bn_layer=True, bn_1st=True, zero_bn=False,\n",
    "            padding=None, bias=False, groups=1, **kwargs):\n",
    "\n",
    "        if padding==None: padding = ks//2\n",
    "#         layers = [('conv', nn.Conv2d(ni, nf, ks, stride=stride, \n",
    "#                 padding=padding, bias=bias, groups=groups))]\n",
    "        if ks == 1:  layers = [('conv', nn.Conv2d(ni, nf, ks, stride=stride,bias=bias))]\n",
    "        else:  layers = [('conv_twist', conv_twist(ni, nf, stride=stride))]\n",
    "\n",
    "        act_bn = [('act_fn', act_fn)] if act else []\n",
    "        if bn_layer:\n",
    "            bn = nn.BatchNorm2d(nf)\n",
    "            nn.init.constant_(bn.weight, 0. if zero_bn else 1.)\n",
    "            act_bn += [('bn', bn)]\n",
    "        if bn_1st: act_bn.reverse()\n",
    "        layers += act_bn\n",
    "        super().__init__(OrderedDict(layers))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class NewResBlock(Module):\n",
    "    def __init__(self, expansion, ni, nh, stride=1, \n",
    "                 conv_layer=ConvLayer, act_fn=act_fn, bn_1st=True,\n",
    "#                  conv_layer=NewLayer, act_fn=act_fn, bn_1st=True,\n",
    "                 pool=nn.AvgPool2d(2, ceil_mode=True), sa=False,sym=False, zero_bn=True):\n",
    "        nf,ni = nh*expansion,ni*expansion\n",
    "        conv_layer = NewLayer\n",
    "        self.reduce = noop if stride==1 else pool\n",
    "        layers  = [(f\"conv_0\", conv_layer(ni, nh, 3, stride=stride, act_fn=act_fn, bn_1st=bn_1st)),\n",
    "                   (f\"conv_1\", conv_layer(nh, nf, 3, zero_bn=zero_bn, act=False, bn_1st=bn_1st))\n",
    "        ] if expansion == 1 else [\n",
    "                   (f\"conv_0\",conv_layer(ni, nh, 1, act_fn=act_fn, bn_1st=bn_1st)),\n",
    "                   (f\"conv_1\",conv_layer(nh, nh, 3, stride=1, act_fn=act_fn, bn_1st=bn_1st)), #!!!\n",
    "                   (f\"conv_2\",conv_layer(nh, nf, 1, zero_bn=zero_bn, act=False, bn_1st=bn_1st))\n",
    "        ]\n",
    "        if sa: layers.append(('sa', SimpleSelfAttention(nf,ks=1,sym=sym)))\n",
    "        self.convs = nn.Sequential(OrderedDict(layers))\n",
    "        self.idconv = noop if ni==nf else conv_layer(ni, nf, 1, act=False, bn_1st=bn_1st)\n",
    "        self.merge =act_fn\n",
    "\n",
    "    def forward(self, x): \n",
    "        o = self.reduce(x)\n",
    "        return self.merge(self.convs(o) + self.idconv(o))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NewResBlock(\n",
       "  (convs): Sequential(\n",
       "    (conv_0): NewLayer(\n",
       "      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (act_fn): ReLU(inplace=True)\n",
       "    )\n",
       "    (conv_1): NewLayer(\n",
       "      (conv_twist): conv_twist(\n",
       "        (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (convx): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (convy): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (act_fn): ReLU(inplace=True)\n",
       "    )\n",
       "    (conv_2): NewLayer(\n",
       "      (conv): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (idconv): NewLayer(\n",
       "    (conv): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "    (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  )\n",
       "  (merge): ReLU(inplace=True)\n",
       ")"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# hide\n",
    "NewResBlock(4, 64, 128)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Constructor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pool = MaxBlurPool2d(3, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Net(c_out=10, layers=[3,4,6,3], expansion=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.block = NewResBlock\n",
    "model.pool = pool\n",
    "model.stem_pool = pool\n",
    "model.stem_sizes = [3,32,64,64]\n",
    "model.act_fn= Mish()\n",
    "model.sa = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## repr model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  model Net\n",
       "  (stem): Sequential(\n",
       "    (conv_0): ConvLayer(\n",
       "      (conv): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (act_fn): Mish()\n",
       "    )\n",
       "    (conv_1): ConvLayer(\n",
       "      (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (act_fn): Mish()\n",
       "    )\n",
       "    (conv_2): ConvLayer(\n",
       "      (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (act_fn): Mish()\n",
       "    )\n",
       "    (stem_pool): MaxBlurPool2d()\n",
       "  )\n",
       "  (body): Sequential(\n",
       "    (l_0): Sequential(\n",
       "      (bl_0): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): NewLayer(\n",
       "            (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): NewLayer(\n",
       "            (conv_twist): conv_twist(\n",
       "              (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (convx): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (convy): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            )\n",
       "            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): NewLayer(\n",
       "            (conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (idconv): NewLayer(\n",
       "          (conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_1): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): NewLayer(\n",
       "            (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): NewLayer(\n",
       "            (conv_twist): conv_twist(\n",
       "              (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (convx): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (convy): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            )\n",
       "            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): NewLayer(\n",
       "            (conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_2): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): NewLayer(\n",
       "            (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): NewLayer(\n",
       "            (conv_twist): conv_twist(\n",
       "              (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (convx): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (convy): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            )\n",
       "            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): NewLayer(\n",
       "            (conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "          (sa): SimpleSelfAttention(\n",
       "            (conv): Conv1d(256, 256, kernel_size=(1,), stride=(1,), bias=False)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "    )\n",
       "    (l_1): Sequential(\n",
       "      (bl_0): NewResBlock(\n",
       "        (reduce): MaxBlurPool2d()\n",
       "        (convs): Sequential(\n",
       "          (conv_0): NewLayer(\n",
       "            (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): NewLayer(\n",
       "            (conv_twist): conv_twist(\n",
       "              (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (convx): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (convy): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            )\n",
       "            (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): NewLayer(\n",
       "            (conv): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (idconv): NewLayer(\n",
       "          (conv): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_1): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): NewLayer(\n",
       "            (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): NewLayer(\n",
       "            (conv_twist): conv_twist(\n",
       "              (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (convx): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (convy): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            )\n",
       "            (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): NewLayer(\n",
       "            (conv): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_2): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): NewLayer(\n",
       "            (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): NewLayer(\n",
       "            (conv_twist): conv_twist(\n",
       "              (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (convx): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (convy): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            )\n",
       "            (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): NewLayer(\n",
       "            (conv): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_3): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): NewLayer(\n",
       "            (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): NewLayer(\n",
       "            (conv_twist): conv_twist(\n",
       "              (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (convx): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (convy): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            )\n",
       "            (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): NewLayer(\n",
       "            (conv): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "    )\n",
       "    (l_2): Sequential(\n",
       "      (bl_0): NewResBlock(\n",
       "        (reduce): MaxBlurPool2d()\n",
       "        (convs): Sequential(\n",
       "          (conv_0): NewLayer(\n",
       "            (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): NewLayer(\n",
       "            (conv_twist): conv_twist(\n",
       "              (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (convx): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (convy): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            )\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): NewLayer(\n",
       "            (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (idconv): NewLayer(\n",
       "          (conv): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_1): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): NewLayer(\n",
       "            (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): NewLayer(\n",
       "            (conv_twist): conv_twist(\n",
       "              (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (convx): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (convy): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            )\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): NewLayer(\n",
       "            (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_2): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): NewLayer(\n",
       "            (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): NewLayer(\n",
       "            (conv_twist): conv_twist(\n",
       "              (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (convx): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (convy): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            )\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): NewLayer(\n",
       "            (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_3): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): NewLayer(\n",
       "            (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): NewLayer(\n",
       "            (conv_twist): conv_twist(\n",
       "              (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (convx): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (convy): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            )\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): NewLayer(\n",
       "            (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_4): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): NewLayer(\n",
       "            (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): NewLayer(\n",
       "            (conv_twist): conv_twist(\n",
       "              (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (convx): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (convy): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            )\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): NewLayer(\n",
       "            (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_5): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): NewLayer(\n",
       "            (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): NewLayer(\n",
       "            (conv_twist): conv_twist(\n",
       "              (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (convx): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (convy): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            )\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): NewLayer(\n",
       "            (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "    )\n",
       "    (l_3): Sequential(\n",
       "      (bl_0): NewResBlock(\n",
       "        (reduce): MaxBlurPool2d()\n",
       "        (convs): Sequential(\n",
       "          (conv_0): NewLayer(\n",
       "            (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): NewLayer(\n",
       "            (conv_twist): conv_twist(\n",
       "              (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (convx): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (convy): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            )\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): NewLayer(\n",
       "            (conv): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (idconv): NewLayer(\n",
       "          (conv): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_1): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): NewLayer(\n",
       "            (conv): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): NewLayer(\n",
       "            (conv_twist): conv_twist(\n",
       "              (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (convx): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (convy): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            )\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): NewLayer(\n",
       "            (conv): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_2): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): NewLayer(\n",
       "            (conv): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): NewLayer(\n",
       "            (conv_twist): conv_twist(\n",
       "              (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (convx): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (convy): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            )\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): NewLayer(\n",
       "            (conv): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (head): Sequential(\n",
       "    (pool): AdaptiveAvgPool2d(output_size=1)\n",
       "    (flat): Flatten()\n",
       "    (fc): Linear(in_features=2048, out_features=10, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# hide\n",
    "model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (conv_0): ConvLayer(\n",
       "    (conv): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "    (bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (act_fn): Mish()\n",
       "  )\n",
       "  (conv_1): ConvLayer(\n",
       "    (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "    (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (act_fn): Mish()\n",
       "  )\n",
       "  (conv_2): ConvLayer(\n",
       "    (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "    (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (act_fn): Mish()\n",
       "  )\n",
       "  (stem_pool): MaxBlurPool2d()\n",
       ")"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.stem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (l_0): Sequential(\n",
       "    (bl_0): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): NewLayer(\n",
       "          (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): NewLayer(\n",
       "          (conv_twist): conv_twist(\n",
       "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (convx): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (convy): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          )\n",
       "          (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): NewLayer(\n",
       "          (conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (idconv): NewLayer(\n",
       "        (conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_1): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): NewLayer(\n",
       "          (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): NewLayer(\n",
       "          (conv_twist): conv_twist(\n",
       "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (convx): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (convy): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          )\n",
       "          (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): NewLayer(\n",
       "          (conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_2): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): NewLayer(\n",
       "          (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): NewLayer(\n",
       "          (conv_twist): conv_twist(\n",
       "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (convx): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (convy): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          )\n",
       "          (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): NewLayer(\n",
       "          (conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (sa): SimpleSelfAttention(\n",
       "          (conv): Conv1d(256, 256, kernel_size=(1,), stride=(1,), bias=False)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "  )\n",
       "  (l_1): Sequential(\n",
       "    (bl_0): NewResBlock(\n",
       "      (reduce): MaxBlurPool2d()\n",
       "      (convs): Sequential(\n",
       "        (conv_0): NewLayer(\n",
       "          (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): NewLayer(\n",
       "          (conv_twist): conv_twist(\n",
       "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (convx): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (convy): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          )\n",
       "          (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): NewLayer(\n",
       "          (conv): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (idconv): NewLayer(\n",
       "        (conv): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_1): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): NewLayer(\n",
       "          (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): NewLayer(\n",
       "          (conv_twist): conv_twist(\n",
       "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (convx): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (convy): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          )\n",
       "          (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): NewLayer(\n",
       "          (conv): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_2): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): NewLayer(\n",
       "          (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): NewLayer(\n",
       "          (conv_twist): conv_twist(\n",
       "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (convx): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (convy): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          )\n",
       "          (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): NewLayer(\n",
       "          (conv): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_3): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): NewLayer(\n",
       "          (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): NewLayer(\n",
       "          (conv_twist): conv_twist(\n",
       "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (convx): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (convy): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          )\n",
       "          (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): NewLayer(\n",
       "          (conv): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "  )\n",
       "  (l_2): Sequential(\n",
       "    (bl_0): NewResBlock(\n",
       "      (reduce): MaxBlurPool2d()\n",
       "      (convs): Sequential(\n",
       "        (conv_0): NewLayer(\n",
       "          (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): NewLayer(\n",
       "          (conv_twist): conv_twist(\n",
       "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (convx): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (convy): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          )\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): NewLayer(\n",
       "          (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (idconv): NewLayer(\n",
       "        (conv): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_1): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): NewLayer(\n",
       "          (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): NewLayer(\n",
       "          (conv_twist): conv_twist(\n",
       "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (convx): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (convy): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          )\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): NewLayer(\n",
       "          (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_2): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): NewLayer(\n",
       "          (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): NewLayer(\n",
       "          (conv_twist): conv_twist(\n",
       "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (convx): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (convy): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          )\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): NewLayer(\n",
       "          (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_3): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): NewLayer(\n",
       "          (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): NewLayer(\n",
       "          (conv_twist): conv_twist(\n",
       "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (convx): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (convy): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          )\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): NewLayer(\n",
       "          (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_4): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): NewLayer(\n",
       "          (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): NewLayer(\n",
       "          (conv_twist): conv_twist(\n",
       "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (convx): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (convy): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          )\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): NewLayer(\n",
       "          (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_5): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): NewLayer(\n",
       "          (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): NewLayer(\n",
       "          (conv_twist): conv_twist(\n",
       "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (convx): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (convy): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          )\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): NewLayer(\n",
       "          (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "  )\n",
       "  (l_3): Sequential(\n",
       "    (bl_0): NewResBlock(\n",
       "      (reduce): MaxBlurPool2d()\n",
       "      (convs): Sequential(\n",
       "        (conv_0): NewLayer(\n",
       "          (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): NewLayer(\n",
       "          (conv_twist): conv_twist(\n",
       "            (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (convx): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (convy): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          )\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): NewLayer(\n",
       "          (conv): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (idconv): NewLayer(\n",
       "        (conv): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_1): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): NewLayer(\n",
       "          (conv): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): NewLayer(\n",
       "          (conv_twist): conv_twist(\n",
       "            (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (convx): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (convy): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          )\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): NewLayer(\n",
       "          (conv): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_2): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): NewLayer(\n",
       "          (conv): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): NewLayer(\n",
       "          (conv_twist): conv_twist(\n",
       "            (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (convx): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (convy): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          )\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): NewLayer(\n",
       "          (conv): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.body"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (pool): AdaptiveAvgPool2d(output_size=1)\n",
       "  (flat): Flatten()\n",
       "  (fc): Linear(in_features=2048, out_features=10, bias=True)\n",
       ")"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = 0.004\n",
    "epochs = 5\n",
    "moms = (0.95,0.95)\n",
    "start_pct = 0.72\n",
    "size=128\n",
    "bs=64\n",
    "mixup=0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lr find"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data path   /notebooks/data/imagewoof2\n",
      "Learn path /notebooks/data/imagewoof2\n"
     ]
    }
   ],
   "source": [
    "learn = get_learn(model=model,size=size,bs=bs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "        <style>\n",
       "            /* Turns off some styling */\n",
       "            progress {\n",
       "                /* gets rid of default border in Firefox and Opera. */\n",
       "                border: none;\n",
       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "                background-size: auto;\n",
       "            }\n",
       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
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       "      <progress value='0' class='' max='1', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      0.00% [0/1 00:00<00:00]\n",
       "    </div>\n",
       "    \n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table><p>\n",
       "\n",
       "    <div>\n",
       "        <style>\n",
       "            /* Turns off some styling */\n",
       "            progress {\n",
       "                /* gets rid of default border in Firefox and Opera. */\n",
       "                border: none;\n",
       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "                background-size: auto;\n",
       "            }\n",
       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "                background: #F44336;\n",
       "            }\n",
       "        </style>\n",
       "      <progress value='92' class='' max='141', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      65.25% [92/141 00:36<00:19 7.4237]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "set state called\n",
      "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
     ]
    }
   ],
   "source": [
    "learn.lr_find()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# epochs 5 lr 0.008 750-754-749"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr  = 8e-3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data path   /notebooks/data/imagewoof2\n",
      "Learn path /notebooks/data/imagewoof2\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.966241</td>\n",
       "      <td>2.355271</td>\n",
       "      <td>0.342326</td>\n",
       "      <td>0.829982</td>\n",
       "      <td>01:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.712882</td>\n",
       "      <td>1.544966</td>\n",
       "      <td>0.548740</td>\n",
       "      <td>0.922118</td>\n",
       "      <td>01:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.525972</td>\n",
       "      <td>1.690973</td>\n",
       "      <td>0.474675</td>\n",
       "      <td>0.904556</td>\n",
       "      <td>01:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.389795</td>\n",
       "      <td>1.271689</td>\n",
       "      <td>0.665818</td>\n",
       "      <td>0.953169</td>\n",
       "      <td>01:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.181057</td>\n",
       "      <td>1.115653</td>\n",
       "      <td>0.750064</td>\n",
       "      <td>0.969458</td>\n",
       "      <td>01:11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = get_learn(model=model,size=size,bs=bs,mixup=mixup)\n",
    "learn.fit_fc(epochs, lr, moms,start_pct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_metrics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data path   /notebooks/data/imagewoof2\n",
      "Learn path /notebooks/data/imagewoof2\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.982817</td>\n",
       "      <td>2.583601</td>\n",
       "      <td>0.284805</td>\n",
       "      <td>0.759735</td>\n",
       "      <td>01:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.682359</td>\n",
       "      <td>1.528550</td>\n",
       "      <td>0.561212</td>\n",
       "      <td>0.926699</td>\n",
       "      <td>01:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.507325</td>\n",
       "      <td>1.548863</td>\n",
       "      <td>0.555612</td>\n",
       "      <td>0.938661</td>\n",
       "      <td>01:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.338856</td>\n",
       "      <td>1.262519</td>\n",
       "      <td>0.671927</td>\n",
       "      <td>0.956477</td>\n",
       "      <td>01:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.150432</td>\n",
       "      <td>1.100051</td>\n",
       "      <td>0.754645</td>\n",
       "      <td>0.967931</td>\n",
       "      <td>01:10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = get_learn(model=model,size=size,bs=bs,mixup=mixup)\n",
    "learn.fit_fc(epochs, lr, moms,start_pct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_metrics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data path   /notebooks/data/imagewoof2\n",
      "Learn path /notebooks/data/imagewoof2\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.983218</td>\n",
       "      <td>2.324211</td>\n",
       "      <td>0.317129</td>\n",
       "      <td>0.790023</td>\n",
       "      <td>01:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.739398</td>\n",
       "      <td>1.682637</td>\n",
       "      <td>0.503181</td>\n",
       "      <td>0.916773</td>\n",
       "      <td>01:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.560594</td>\n",
       "      <td>1.647981</td>\n",
       "      <td>0.497837</td>\n",
       "      <td>0.913719</td>\n",
       "      <td>01:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.419692</td>\n",
       "      <td>1.280064</td>\n",
       "      <td>0.665055</td>\n",
       "      <td>0.955205</td>\n",
       "      <td>01:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.184725</td>\n",
       "      <td>1.117846</td>\n",
       "      <td>0.749300</td>\n",
       "      <td>0.970221</td>\n",
       "      <td>01:11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = get_learn(model=model,size=size,bs=bs,mixup=mixup)\n",
    "learn.fit_fc(epochs, lr, moms,start_pct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_metrics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc = np.array([0.750064, 0.754645, 0.749300])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7513363333333333, 0.0023602796915243606)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc.mean(), acc.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# epochs 20 8639"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = 0.008\n",
    "mixup=0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data path   /notebooks/data/imagewoof2\n",
      "Learn path /notebooks/data/imagewoof2\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2.033044</td>\n",
       "      <td>2.413331</td>\n",
       "      <td>0.279206</td>\n",
       "      <td>0.840417</td>\n",
       "      <td>01:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.824975</td>\n",
       "      <td>1.666244</td>\n",
       "      <td>0.503181</td>\n",
       "      <td>0.903029</td>\n",
       "      <td>01:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.648385</td>\n",
       "      <td>1.678949</td>\n",
       "      <td>0.505727</td>\n",
       "      <td>0.921100</td>\n",
       "      <td>01:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.540806</td>\n",
       "      <td>1.341313</td>\n",
       "      <td>0.644439</td>\n",
       "      <td>0.951642</td>\n",
       "      <td>01:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.426934</td>\n",
       "      <td>1.386314</td>\n",
       "      <td>0.628404</td>\n",
       "      <td>0.935353</td>\n",
       "      <td>01:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.366867</td>\n",
       "      <td>1.163470</td>\n",
       "      <td>0.724866</td>\n",
       "      <td>0.962586</td>\n",
       "      <td>01:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.301847</td>\n",
       "      <td>1.394628</td>\n",
       "      <td>0.606007</td>\n",
       "      <td>0.942988</td>\n",
       "      <td>01:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1.263568</td>\n",
       "      <td>1.093776</td>\n",
       "      <td>0.756172</td>\n",
       "      <td>0.967931</td>\n",
       "      <td>01:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.216472</td>\n",
       "      <td>1.314232</td>\n",
       "      <td>0.649020</td>\n",
       "      <td>0.954696</td>\n",
       "      <td>01:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1.187703</td>\n",
       "      <td>1.031111</td>\n",
       "      <td>0.775006</td>\n",
       "      <td>0.972258</td>\n",
       "      <td>01:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>1.149947</td>\n",
       "      <td>1.115963</td>\n",
       "      <td>0.743446</td>\n",
       "      <td>0.969967</td>\n",
       "      <td>01:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>1.136042</td>\n",
       "      <td>0.985693</td>\n",
       "      <td>0.800458</td>\n",
       "      <td>0.975312</td>\n",
       "      <td>01:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>1.123594</td>\n",
       "      <td>1.186430</td>\n",
       "      <td>0.717740</td>\n",
       "      <td>0.956732</td>\n",
       "      <td>01:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>1.083386</td>\n",
       "      <td>1.037488</td>\n",
       "      <td>0.781624</td>\n",
       "      <td>0.967931</td>\n",
       "      <td>01:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1.050268</td>\n",
       "      <td>1.015548</td>\n",
       "      <td>0.790023</td>\n",
       "      <td>0.974548</td>\n",
       "      <td>01:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1.022487</td>\n",
       "      <td>0.947441</td>\n",
       "      <td>0.823110</td>\n",
       "      <td>0.976075</td>\n",
       "      <td>01:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.983307</td>\n",
       "      <td>0.932026</td>\n",
       "      <td>0.826164</td>\n",
       "      <td>0.981166</td>\n",
       "      <td>01:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0.908805</td>\n",
       "      <td>0.871237</td>\n",
       "      <td>0.850598</td>\n",
       "      <td>0.981166</td>\n",
       "      <td>01:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0.858699</td>\n",
       "      <td>0.845283</td>\n",
       "      <td>0.863578</td>\n",
       "      <td>0.983965</td>\n",
       "      <td>01:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.832820</td>\n",
       "      <td>0.843645</td>\n",
       "      <td>0.864851</td>\n",
       "      <td>0.982438</td>\n",
       "      <td>01:11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = get_learn(model=model,size=size,bs=bs,mixup=mixup)\n",
    "learn.fit_fc(epochs, lr, moms,start_pct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_metrics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data path   /notebooks/data/imagewoof2\n",
      "Learn path /notebooks/data/imagewoof2\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2.051791</td>\n",
       "      <td>2.096750</td>\n",
       "      <td>0.329855</td>\n",
       "      <td>0.817765</td>\n",
       "      <td>01:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.819863</td>\n",
       "      <td>1.541649</td>\n",
       "      <td>0.540341</td>\n",
       "      <td>0.930517</td>\n",
       "      <td>01:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.647785</td>\n",
       "      <td>1.478515</td>\n",
       "      <td>0.576483</td>\n",
       "      <td>0.934334</td>\n",
       "      <td>01:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.535495</td>\n",
       "      <td>1.375381</td>\n",
       "      <td>0.630440</td>\n",
       "      <td>0.950115</td>\n",
       "      <td>01:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.446517</td>\n",
       "      <td>1.677044</td>\n",
       "      <td>0.528379</td>\n",
       "      <td>0.936371</td>\n",
       "      <td>01:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.380019</td>\n",
       "      <td>1.198812</td>\n",
       "      <td>0.706032</td>\n",
       "      <td>0.960041</td>\n",
       "      <td>01:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.334624</td>\n",
       "      <td>1.348320</td>\n",
       "      <td>0.648002</td>\n",
       "      <td>0.936116</td>\n",
       "      <td>01:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1.262918</td>\n",
       "      <td>1.105916</td>\n",
       "      <td>0.745737</td>\n",
       "      <td>0.969712</td>\n",
       "      <td>01:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.216332</td>\n",
       "      <td>1.082583</td>\n",
       "      <td>0.771698</td>\n",
       "      <td>0.971240</td>\n",
       "      <td>01:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1.175984</td>\n",
       "      <td>1.029488</td>\n",
       "      <td>0.784424</td>\n",
       "      <td>0.972003</td>\n",
       "      <td>01:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>1.153041</td>\n",
       "      <td>1.234762</td>\n",
       "      <td>0.696360</td>\n",
       "      <td>0.964113</td>\n",
       "      <td>01:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>1.117275</td>\n",
       "      <td>0.989114</td>\n",
       "      <td>0.802749</td>\n",
       "      <td>0.977602</td>\n",
       "      <td>01:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>1.105340</td>\n",
       "      <td>1.067769</td>\n",
       "      <td>0.769407</td>\n",
       "      <td>0.969458</td>\n",
       "      <td>01:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>1.077254</td>\n",
       "      <td>0.993925</td>\n",
       "      <td>0.797913</td>\n",
       "      <td>0.976075</td>\n",
       "      <td>01:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1.078567</td>\n",
       "      <td>1.031483</td>\n",
       "      <td>0.779079</td>\n",
       "      <td>0.975057</td>\n",
       "      <td>01:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1.036915</td>\n",
       "      <td>0.940059</td>\n",
       "      <td>0.816238</td>\n",
       "      <td>0.977857</td>\n",
       "      <td>01:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.979548</td>\n",
       "      <td>0.981941</td>\n",
       "      <td>0.808348</td>\n",
       "      <td>0.972258</td>\n",
       "      <td>01:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0.921617</td>\n",
       "      <td>0.870986</td>\n",
       "      <td>0.849835</td>\n",
       "      <td>0.981929</td>\n",
       "      <td>01:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0.865582</td>\n",
       "      <td>0.853171</td>\n",
       "      <td>0.857725</td>\n",
       "      <td>0.984729</td>\n",
       "      <td>01:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.838275</td>\n",
       "      <td>0.846854</td>\n",
       "      <td>0.863069</td>\n",
       "      <td>0.984474</td>\n",
       "      <td>01:11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = get_learn(model=model,size=size,bs=bs,mixup=mixup)\n",
    "learn.fit_fc(epochs, lr, moms,start_pct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_metrics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc = np.array([0.864851, 0.863069])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8639600000000001, 0.0008910000000000307)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc.mean(), acc.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = 0.007"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data path   /notebooks/data/imagewoof2\n",
      "Learn path /notebooks/data/imagewoof2\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2.092022</td>\n",
       "      <td>2.202029</td>\n",
       "      <td>0.259099</td>\n",
       "      <td>0.783151</td>\n",
       "      <td>01:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.876156</td>\n",
       "      <td>1.666526</td>\n",
       "      <td>0.470858</td>\n",
       "      <td>0.912191</td>\n",
       "      <td>01:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.686609</td>\n",
       "      <td>1.729666</td>\n",
       "      <td>0.477984</td>\n",
       "      <td>0.909901</td>\n",
       "      <td>01:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.571561</td>\n",
       "      <td>1.442288</td>\n",
       "      <td>0.596080</td>\n",
       "      <td>0.940443</td>\n",
       "      <td>01:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.472482</td>\n",
       "      <td>1.558673</td>\n",
       "      <td>0.549504</td>\n",
       "      <td>0.930262</td>\n",
       "      <td>01:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.410722</td>\n",
       "      <td>1.217631</td>\n",
       "      <td>0.703487</td>\n",
       "      <td>0.956477</td>\n",
       "      <td>01:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.346763</td>\n",
       "      <td>1.175040</td>\n",
       "      <td>0.719776</td>\n",
       "      <td>0.959786</td>\n",
       "      <td>01:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1.276313</td>\n",
       "      <td>1.088257</td>\n",
       "      <td>0.761008</td>\n",
       "      <td>0.967167</td>\n",
       "      <td>01:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.257796</td>\n",
       "      <td>1.297571</td>\n",
       "      <td>0.674726</td>\n",
       "      <td>0.955714</td>\n",
       "      <td>01:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1.219484</td>\n",
       "      <td>1.076940</td>\n",
       "      <td>0.770425</td>\n",
       "      <td>0.966404</td>\n",
       "      <td>01:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>1.179843</td>\n",
       "      <td>1.248394</td>\n",
       "      <td>0.681344</td>\n",
       "      <td>0.959023</td>\n",
       "      <td>01:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>1.146754</td>\n",
       "      <td>1.033296</td>\n",
       "      <td>0.780860</td>\n",
       "      <td>0.967676</td>\n",
       "      <td>01:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>1.118251</td>\n",
       "      <td>1.067300</td>\n",
       "      <td>0.765589</td>\n",
       "      <td>0.969203</td>\n",
       "      <td>01:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>1.105149</td>\n",
       "      <td>0.983098</td>\n",
       "      <td>0.803767</td>\n",
       "      <td>0.975566</td>\n",
       "      <td>01:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1.079094</td>\n",
       "      <td>1.046762</td>\n",
       "      <td>0.770934</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>01:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1.040225</td>\n",
       "      <td>0.947715</td>\n",
       "      <td>0.814457</td>\n",
       "      <td>0.977857</td>\n",
       "      <td>01:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.974672</td>\n",
       "      <td>0.939471</td>\n",
       "      <td>0.821838</td>\n",
       "      <td>0.975312</td>\n",
       "      <td>01:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0.927258</td>\n",
       "      <td>0.876603</td>\n",
       "      <td>0.849326</td>\n",
       "      <td>0.983202</td>\n",
       "      <td>01:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0.872151</td>\n",
       "      <td>0.851138</td>\n",
       "      <td>0.858997</td>\n",
       "      <td>0.982947</td>\n",
       "      <td>01:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.854163</td>\n",
       "      <td>0.846512</td>\n",
       "      <td>0.860524</td>\n",
       "      <td>0.983711</td>\n",
       "      <td>01:12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = get_learn(model=model,size=size,bs=bs,mixup=mixup)\n",
    "learn.fit_fc(epochs, lr, moms,start_pct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_metrics()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
